Igor L. Markov, « Reevaluating Google's Reinforcement Learning for IC Macro Placement » [archive du ], sur Communications of the ACM, Association for Computing Machinery, : « A 2021 paper in Nature by Mirhoseini, Goldie, et al. about the use of reinforcement learning (RL) in the physical design of silicon chips raised eyebrows, drew critical media coverage, and stirred up controversy due to poorly documented claims. … This meta-analysis discusses the reproduction and evaluation of results in the Nature paper by Mirhoseini, Goldie, et al., as well as the validity of methods, results, and claims. ».
Adam Santoro, David Raposo, David G. T. Barrett et Mateusz Malinowski, « A simple neural network module for relational reasoning », arXiv:1706.01427 [cs], (lire en ligne, consulté le ).
Nicholas Watters, Andrea Tacchetti, Theophane Weber et Razvan Pascanu, « Visual Interaction Networks », arXiv:1706.01433 [cs], (lire en ligne, consulté le ).
(en) Sam Shead, « DeepMind's CEO told Prince Harry his AI lab now employs 700 staff: 'It's really the biggest collection of brain power anywhere in the world on this topic' », Business Insider, (lire en ligne).
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum et Samuel J. Gershman, « Building Machines That Learn and Think Like People », Behavioral and Brain Sciences, , p. 1–101 (ISSN0140-525X et 1469-1825, DOI10.1017/S0140525X16001837, lire en ligne, consulté le ).
(en) Volodymyr Mnih, Koray Kavukcuoglu et David Silver, « Human-level control through deep reinforcement learning », Nature, vol. 518, (DOI10.1038/nature14236, lire en ligne).
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum et Samuel J. Gershman, « Building Machines That Learn and Think Like People », Behavioral and Brain Sciences, , p. 1–101 (ISSN0140-525X et 1469-1825, DOI10.1017/S0140525X16001837, lire en ligne, consulté le ).
(en) Ewen Callaway, « ‘It will change everything': DeepMind's AI makes gigantic leap in solving protein structures », Nature, vol. 588, no 7837, , p. 203–204 (DOI10.1038/d41586-020-03348-4, lire en ligne, consulté le ).
(en) Ewen Callaway, « DeepMind's AI predicts structures for a vast trove of proteins », Nature, vol. 595, no 7869, , p. 635–635 (DOI10.1038/d41586-021-02025-4, lire en ligne, consulté le ).
(en) Bender EM, Gebru T, McMillan-Major A, Shmitchell S, Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event Canada, ACM, , 610–623 p. (ISBN978-1-4503-8309-7, DOI10.1145/3442188.3445922), « On the Dangers of Stochastic Parrots ».
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum et Samuel J. Gershman, « Building Machines That Learn and Think Like People », Behavioral and Brain Sciences, , p. 1–101 (ISSN0140-525X et 1469-1825, DOI10.1017/S0140525X16001837, lire en ligne, consulté le ).
(en) Volodymyr Mnih, Koray Kavukcuoglu et David Silver, « Human-level control through deep reinforcement learning », Nature, vol. 518, (DOI10.1038/nature14236, lire en ligne).
(en) Ewen Callaway, « ‘It will change everything': DeepMind's AI makes gigantic leap in solving protein structures », Nature, vol. 588, no 7837, , p. 203–204 (DOI10.1038/d41586-020-03348-4, lire en ligne, consulté le ).
(en) Ewen Callaway, « DeepMind's AI predicts structures for a vast trove of proteins », Nature, vol. 595, no 7869, , p. 635–635 (DOI10.1038/d41586-021-02025-4, lire en ligne, consulté le ).
Igor L. Markov, « Reevaluating Google's Reinforcement Learning for IC Macro Placement » [archive du ], sur Communications of the ACM, Association for Computing Machinery, : « A 2021 paper in Nature by Mirhoseini, Goldie, et al. about the use of reinforcement learning (RL) in the physical design of silicon chips raised eyebrows, drew critical media coverage, and stirred up controversy due to poorly documented claims. … This meta-analysis discusses the reproduction and evaluation of results in the Nature paper by Mirhoseini, Goldie, et al., as well as the validity of methods, results, and claims. ».